London hire bike jouneys mapped and animated

This short animation by Jo Wood at City University aims to help us see the patterns in the mass of data arising from London’s bicycle hire scheme (often referred to as Boris Bikes, although the scheme was devised by the previous mayor Ken Livingstone). For those unfamiliar with this scheme, you can walk up to a bike rack, put in your credit card or pre-paid details, take a bike and then leave it at another rack somewhere else. Little trucks nip around the city redistributing the bikes to make sure they don’t all end up in one place.

Screenshot from the animation

At first glance I was baffled by the time aspect. What was changing over time? Were these real bike journeys at different times of the day? I was confused because I always click “play” before I read the text (also the reason why I can’t understand our TV remote control at home). Eventually I realised that it starts off showing all journeys, though the individual trails are simulated and not real people on bikes, and these accumulate over time until about 15 seconds in, when it gradually gets filtered down to showing the more popular routes and ends up with just the key “hubs” illuminated. Prof Wood says this is like “a graphic equaliser”, which is a concept much more familiar to my generation.

It’s a novel approach in quite a subtle way: time is used to show density. Imagine having loads of bivariate normal data and wanting to show the distribution. You could draw a contour plot but this gets nasty as the distribution gets more complex, so why not have an animation showing all the data in a scatterplot, and gradually remove the dots from the less populous regions, moving in by convex hulls until only the mode is still populated. Here’s a rough animation I made with uncorrelated bivariate normal data (n=10,000).

Now, for simple distributions like this, it’s not very useful. But when you get into weird shapes, it could be quite useful. Another way you could imagine it is a 3-D surface with density on the vertical axis, which gradually gets submerged below an opaque “water level” until only the highest peaks are visible.


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